Development of a receptor model for efficient in silico screening of HIV-1 integrase inhibitors

Integrase (IN) is a key viral enzyme for the replication of the type-1 human immunodeficiency virus (HIV-1), and as such constitutes a relevant therapeutic target for the development of anti-HIV agents. However, the lack of crystallographic data of HIV IN complexed with the corresponding viral DNA has historically hindered the application of modern structure-based drug design techniques to the discovery of new potent IN inhibitors (INIs). Consequently, the development and validation of reliable HIV IN structural models that may be useful for the screening of large databases of chemical compounds is of particular interest. In this study, four HIV-1 IN homology models were evaluated respect to their capability to predict the inhibition potency of a training set comprising 36 previously reported INIs with IC₅₀ values in the low nanomolar to the high micromolar range. Also, 9 inactive structurally related compounds were included in this training set. In addition, a crystallographic structure of the IN-DNA complex corresponding to the prototype foamy virus (PFV) was also evaluated as structural model for the screening of inhibitors. The applicability of high throughput screening techniques, such as blind and ligand-guided exhaustive rigid docking was assessed. The receptor models were also refined by molecular dynamics and clustering techniques to assess protein sidechain flexibility and solvent effect on inhibitor binding. Among the studied models, we conclude that the one derived from the X-ray structure of the PFV integrase exhibited the best performance to rank the potencies of the compounds in the training set, with the predictive power being further improved by explicitly modeling five water molecules within the catalytic side of IN. Also, accounting for protein sidechain flexibility enhanced the prediction of inhibition potencies among the studied compounds. Finally, an interaction fingerprint pattern was established for the fast identification of potent IN inhibitors. In conclusion, we report an exhaustively validated receptor model if IN that is useful for the efficient screening of large chemical compounds databases in the search of potent HIV-1 IN inhibitors.     

A CoMFA study of COX-2 inhibitors with receptor based alignment

A diverse set of 53 cyclooxygenase-2 (COX-2) inhibitors which were aligned in two different ways were subjected to CoMFA analysis. The first method of alignment of the molecules was based on the binding information sourced from the crystallographic study, from which CoMFA Model 1 was derived. The second mode of alignment was generated by docking the inhibitors in the binding pocket using the DOCK and AFFINITY suite of programs; this gave a second model. The CoMFA Model 2 was slightly better than Model 1 in terms of the statistical parameters r(2) and q(2). The two models could predict very well the activity of a test set of diverse molecules, with a predictive r(2) of 0.593 and 0.768, respectively. Besides the QSAR results, the docking studies give a deep insight into the H-bonding interactions between the inhibitors and residues in the active site of the enzyme, which can be exploited in designing better inhibitors. Useful ideas on activity improvement could be gleaned from these models.     

3D-QSAR and molecular docking studies of 1,3,5-triazene-2,4-diamine derivatives against r-RNA: novel bacterial translation inhibitors

Aminoglycoside mimetics inhibit bacterial translation by interfering with the ribosomal decoding site. To elucidate the structural properties of these compounds important for antibacterial activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to a set of 56 aminoglycosides mimetics. The successful CoMFA model yielded the leave-one-out (LOO) cross-validated correlation coefficient (q(2)) of 0.708 and a non-cross-validated correlation coefficient (r(2)) of 0.967. CoMSIA model gave q(2)=0.556 and r(2)=0.935. The CoMFA and CoMSIA models were validated with 36 test set compounds and showed a good r(pred)(2) of 0.624 and 0.640, respectively. Contour maps of the two QSAR approaches show that electronic effects dominantly determine the binding affinities. These obtained results were agreed well with the experimental observations and docking studies. The results not only lead to a better understanding of structural requirements of bacterial translation inhibitors but also can help in the design of novel bacterial translation inhibitors.     

An investigation of structurally diverse carbamates for acetylcholinesterase (AChE) inhibition using 3D-QSAR analysis

In order to identify the essential structural features and physicochemical properties for acetylcholinesterase (AChE) inhibitory activity in some carbamate derivatives, the systematic QSAR (Quantitative Structure Activity Relationship) studies (CoMFA, advance CoMFA and CoMSIA) have been carried out on a series of (total 78 molecules) taking 52 and 26 molecules in training and test set, respectively. Statistically significant 3D-QSAR (three-dimensional Quantitative Structure Activity Relationship) models were developed on training set molecules using CoMFA and CoMSIA and validated against test set compounds. The highly predictive models (CoMFA q(2)=0.733, r(2)=0.967, predictive r(2)=0.732, CoMSIA q(2)=0.641, r(2)=0.936, predictive r(2)=0.812) well explained the variance in binding affinities both for the training and the test set compounds. The generated models suggest that steric, electrostatic and hydrophobic interactions play an important role in describing the variation in binding affinity. In particular the carbamoyl nitrogen should be more electropositive; substitutions on this nitrogen should have high steric bulk and hydrophobicity while the amino nitrogen should be electronegative in order to have better activity. These studies may provide important insights into structural variations leading to the development of novel AChE inhibitors which may be useful in the development of novel molecules for the treatment of Alzheimer's disease.     

Assessing the reliability of a QSAR model's predictions

Quantitative structure activity relationships (QSAR) are one of the well-developed areas in computational chemistry. In this field, many successful predictive models have been developed for various property, activity or toxicity predictions. However, the predictive power of models for new query compounds is often not well characterized. The breadth of applicability of models is often not characterized. In other words, with a given QSAR model and a specific query compound to be predicted, can the model be used reliably for the desired prediction? In this study, we assessed the reliability of QSAR models' prediction on query compounds. Our approach, employing hierarchical clustering, was developed and tested using a test dataset containing 322 organic compounds with fathead minnow acute aquatic toxicity as the activity of interest. The hypothesis of the approach was that if a query compound is more similar to the compounds used to generate the QSAR model, it should be predicted more accurately. Thus, the core of the approach is to determine the relationship between the similarity of query compounds to the training set compounds of the QSAR model and the prediction accuracy given by that model. This relationship determination was achieved by comparing the results given by the two major components of the approach: objects clustering and activity prediction. With the resultant information from the two steps, a direct relationship was shown.     

基于混合选择的GEP算法及其在喹诺酮羧酸类HIV-1整合酶抑制剂活性的QSAR研究中的应用

提出1种混合使用模拟退火和竞赛规则选择算子的改进GEP算法-GEPMS算法。以E-Dragon软件计算、经RM算法筛选得到的7个RDF描述符作为自变量,以抗HIV-1活性IC_(50)值作为因变量,基于GEPMS算法建立关于48种喹诺酮羧酸类化合物的HIV-1整合酶抑制剂活性的QSAR模型。与GEP、GEPSA和v-SVM算法建立的QSAR模型进行比较,本文模型、GEP、GEPSA和v-SVM模型对训练集的计算结果,决定系数R~2分别为0.9667、0.9624、0.9348和0.9711,对验证集的预测结果R~2则分别为0.9565、0.8974、0.9124和0.7656,表明本文的GEPMS模型具有最佳的泛化能力,算法的改进效果明显。     

基于遗传算法的分子场分析方法研究

将遗传算法引入比较分子场分析方法中进行最佳构象的选择。通过遗传算法的优化,可以得到一组统计最优的三维构效关系模型。计算结果表明,通过遗传算法优化得到的最优模型要优于用传统比较分子场分析方法得到的模型;同时,从这个最优构象中我们可以确定这线化合物的活性构象。     

Three-dimensional molecular field analyses of octopaminergic agonists and antagonists for the locust neuronal octopamine receptor class 3

The quantitative structure-activity relationship (QSAR) of a set of 70 octopaminergic agonists and 20 antagonists against octopamine receptor class 3 (OAR3) in locust nervous tissue was analyzed by molecular field analysis (MFA). MFA of these compounds evaluated effectively the energy between a probe and a molecular model at a series of points defined by a rectangular grid. Contour surfaces for the molecular fields are presented. These results provide useful information in the characterization and differentiation of octopaminergic receptor types and subtypes.